Hyperspectral and high-speed imaging are both important for scene representation and understanding. However, simultaneously capturing both hyperspectral and high-speed data is still under-explored. In this work, we propose a high-speed hyperspectral imaging system by integrating compressive sensing sampling with bioinspired neuromorphic sampling. Our system includes a coded aperture snapshot spectral imager capturing moderate-speed hyperspectral measurement frames and a spike camera capturing high-speed grayscale dense spike streams. The two cameras provide complementary dual-modality data for reconstructing high-speed hyperspectral videos (HSV). To effectively synergize the two sampling mechanisms and obtain high-quality HSV, we propose a unified multi-modal reconstruction framework. The framework consists of a Spike Spectral Prior Network for spike-based information extraction and prior regularization, coupled with a dual-modality iterative optimization algorithm for reliable reconstruction. We finally build a hardware prototype to verify the effectiveness of our system and algorithm design. Experiments on both simulated and real data demonstrate the superiority of the proposed approach, where for the first time to our knowledge, high-speed HSV with 30 spectral bands can be captured at a frame rate of up to 20,000 FPS.

高光谱成像和高速成像对于场景表示和理解都很重要。然而，同时捕获高光谱和高速数据仍未得到充分探索。在这项工作中，我们通过将压缩传感采样与仿生神经形态采样相结合，提出了一种高速高光谱成像系统。我们的系统包括一个用于捕获中速高光谱测量帧的编码孔径快照光谱成像仪和一个用于捕获高速灰度密集尖峰流的尖峰相机。这两台相机为重建高速高光谱视频 （HSV） 提供了互补的双模态数据。为了有效地协同两种采样机制并获得高质量的 HSV，我们提出了一个统一的多模态重建框架。该框架由一个用于基于尖峰的信息提取和先验正则化的 Spike Spectral Prior Network 组成，以及用于可靠重建的双模态迭代优化算法。我们最终构建了一个硬件原型，以验证我们的系统和算法设计的有效性。对模拟和真实数据的实验证明了所提出的方法的优越性，据我们所知，首次可以以高达 20,000 FPS 的帧速率捕获具有 30 个光谱波段的高速 HSV。